In our context, deep RL presents a modular computational framework to learn \(\eta _k(t)\)  in Eq. REF . Here, we briefly describe the formulation of the RL problem and the proximal policy optimization (PPO) algorithm [1]}. In RL, at each time step \(t\) , the agent observes some representation of the state of the system, \(s_t \in \mathcal {S}\) , and based on this observation selects an action, \(a_t \in \mathcal {A}\) . The agent receives the reward, \(r_t \in \mathcal {R}\)  as a consequence of the action and the environment enters in a new state \(s_{t+1}\) . Therefore, the interaction of an agent with the environment gives rise to a trajectory as follows
\(\tau = \lbrace  s_0,a_0,r_0,s_1,a_1,r_1,\dots \rbrace .\) 
